粒子群优化平方根强跟踪CKF及应用

提出一种粒子群优化平方根强跟踪容积卡尔曼滤波算法,并将其用于水下应答器辅助航位推算组合导航系统. 以强跟踪滤波器为理论框架,结合容积卡尔曼滤波器,设计了平方根强跟踪容积卡尔曼滤波器. 提出一种改进的粒子群算法,将粒子两两为一对分成若干对,每进化一次后,比较两个粒子的代价函数值,代价函数值较优的粒子,搜索方向侧重于群体历史经验,代价函数较差的粒子,搜索方向侧重于自身历史经验. 将改进的粒子群算法用于求取强跟踪滤波器的渐消因子. 仿真结果表明在系统模型不准确的情况下所提算法依然能够有效跟踪状态变化,比传统的容积卡尔曼滤波器具有更高的滤波精度和稳定性. 

PSO Square Root Strong Tracking Cubature Kalman Filter and its Application

Particle swarm optimization (PSO) square root strong tracking cubature Kalman filter was proposed for underwater transponder positioning/dead reckon (UTP/DR) integrated navigation system. Firstly, the square root strong tracking cubature Kalman filter was designed, which views strong tracking filter (STF) as the basic theory framework. Secondly, a novel particle swarm optimization algorithm has been introduced, the swarm split into a pair of particles, and comparing the performance of two particles of each pair, the better particle search direction focuses on the swarm historical experience, and the other particle search direction focuses on self historical experience. Finally, the novel particle swarm optimization algorithm was used to calculate the strong tracking filter fading factor. The result of simulation showed that under the condition of the system model being not accurate, the proposed algorithm can effectively track the change of state, and shows better filtering accuracy and stability than cubature Kalman filter.